Measuring Bound Attention During Complex Liver Surgery Planning: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The integration of advanced technologies such as augmented reality (AR) and virtual reality (VR) into surgical procedures has garnered significant attention. However, the introduction of these innovations requires thorough evaluation in the context of human-machine interaction. Despite their potential benefits, new technologies can complicate surgical tasks and increase the cognitive load on surgeons, potentially offsetting their intended advantages. It is crucial to evaluate these technologies not only for their functional improvements but also for their impact on the surgeon's workload in clinical settings. A surgical team today must increasingly navigate advanced technologies such as AR and VR, aiming to reduce surgical trauma and enhance patient safety. However, each innovation needs to be evaluated in terms of human-machine interaction. Even if an innovation appears to bring advancements to the field it is applied in, it may complicate the work and increase the surgeon's workload rather than benefiting the surgeon. OBJECTIVE: This study aims to establish a method for objectively determining the additional workload generated using AR or VR glasses in a clinical context for the first time. METHODS: Electroencephalography (EEG) signals were recorded using a passive auditory oddball paradigm while 9 participants performed surgical planning for liver resection across 3 different conditions: (1) using AR glasses, (2) VR glasses, and (3) the conventional planning software on a computer. RESULTS: The electrophysiological results, that is, the potentials evoked by the auditory stimulus, were compared with the subjectively perceived stress of the participants, as determined by the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) questionnaire. The AR condition had the highest scores for mental demand (median 75, IQR 70-85), effort (median 55, IQR 30-65), and frustration (median 40, IQR 15-75) compared with the VR and PC conditions. The analysis of the EEG revealed a trend toward a lower amplitude of the N1 component as well as for the P3 component at the central electrodes in the AR condition, suggesting a higher workload for participants when using AR glasses. In addition, EEG components in the VR condition did not reveal any noticeable differences compared with the EEG components in the conventional planning condition. For the P1 component, the VR condition elicited significantly earlier latencies at the Fz electrode (mean 75.3 ms, SD 25.8 ms) compared with the PC condition (mean 99.4 ms, SD 28.6 ms). CONCLUSIONS: The results suggest a lower stress level when using VR glasses compared with AR glasses, likely due to the 3D visualization of the liver model. Additionally, the alignment between subjectively determined results and objectively determined results confirms the validity of the study design applied in this research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".